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Found 38 Skills
Captures key decisions, questions, follow-ups, and learnings at end of a coding session. Writes a single markdown file per session. Use when done with a session, wrapping up work, running /done, creating a session summary, saving session context, or ending a coding session.
Based on the content of NSFC proposal text and combined with the application code recommendation library, provide you with 5 sets of recommended primary/secondary application codes (Code 1/Code 2) with justifications; output to NSFC-CODE-vYYYYMMDDHHmm.md (read-only, no modification to the proposal)
EDA toolkit. Analyze CSV/Excel/JSON/Parquet files, statistical summaries, distributions, correlations, outliers, missing data, visualizations, markdown reports, for data profiling and insights.
Reporting pipelines for CSV/JSON/Markdown exports with timestamped outputs, summaries, and post-processing.
Create a personal GitHub coding retrospective from a date range and turn it into a short Markdown review. Research commit activity across accessible public and private repositories through the authenticated gh CLI, understand what the relevant repositories and subsystems are for, and write a prose retrospective with stats and highlights. Use when the user asks for a commit review, coding recap, engineering retrospective, GitHub activity story, weekly/monthly/yearly highlights, or a written summary of what their commits achieved.
This skill retrieves upcoming earnings announcements for US stocks using the Financial Modeling Prep (FMP) API. Use this when the user requests earnings calendar data, wants to know which companies are reporting earnings in the upcoming week, or needs a weekly earnings review. The skill focuses on mid-cap and above companies (over $2B market cap) that have significant market impact, organizing the data by date and timing in a clean markdown table format. Supports multiple environments (CLI, Desktop, Web) with flexible API key management.
Conduct in-depth web-based research on given topics, collect and organize materials for subsequent content creation. Automatically detect available web search tools (WebSearch or MCP search tools), fall back to DDGS when no tools are available. Output structured Markdown data summary with source citations.
Maintain JSONL-only profiler performance test cases under csrc/ops/<op>/test in ascend-kernel. Collect data using torch_npu.profiler (with fixed warmup=5 and active=5), aggregate the Total Time(us) from ASCEND_PROFILER_OUTPUT/op_statistic.csv, and output a unified Markdown comparison report (custom operator vs baseline) that includes a DType column. Do not generate perf_cases.json or *_profiler_results.json. Refer to examples/layer_norm_profiler_reference/ for the reference implementation.
Local code review tool for self-inspection before git push. Triggered when users request phrases like "review my code", "check code changes", "review this commit", "review this", "code review", "git review", "help me check my code". Supports reviewing unstaged, staged uncommitted, and committed unpushed changes, and outputs a Markdown review report with scores.
Use when user wants work review or work summary. Triggers on「工作回顾」「日报」「周报」「worklog」「今天做了什么」「本周总结」.
Analyze Huawei Ascend NPU profiling data to discover hidden performance anomalies and produce a detailed model architecture report reverse-engineered from profiling. Trigger on Ascend profiling traces, NPU bottlenecks, device idle gaps, host-device issues, kernel_details.csv / trace_view.json / op_summary / communication.json. Also trigger on "profiling", "step time", "device bubble", "underfeed", "host bound", "device bound", "AICPU", "wait anchor", "kernel gap", "Ascend performance", "model architecture", "layer structure", "forward pass", "model structure". Runs anomaly discovery (bubble detection, wait-anchor, AICPU exposure) alongside model architecture analysis (layer classification, per-layer sub-structure, communication pipeline). Outputs a separate Markdown architecture report alongside anomaly analysis.
Produce a long-form, shareable markdown writeup on whether Claude has regressed on this user's work. A bundled Python script scans `~/.claude/projects/`, computes every metric, and renders a markdown skeleton with tables already filled — in ~2.5s. Claude fills a dozen short narrative placeholders and saves. Writes `./cc-canary-<YYYY-MM-DD>.md` suitable for pasting into a GitHub issue or gist.